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A novel density peaks clustering algorithm for mixed data
Du, Mingjing1; Ding, Shifei1,2; Xue, Yu3
2017-10-01
发表期刊PATTERN RECOGNITION LETTERS
ISSN0167-8655
卷号97页码:46-53
摘要The density peaks clustering (DPC) algorithm is well known for its power on non-spherical distribution data sets. However, it works only on numerical values. This prohibits it from being used to cluster real world data containing categorical values and numerical values. Traditional clustering algorithms for mixed data use a pre-processing based on binary encoding. But such methods destruct the original structure of categorical attributes. Other solutions based on simple matching, such as K-Prototypes, need a userdefined parameter to avoid favoring either type of attribute. In order to overcome these problems, we present a novel clustering algorithm for mixed data, called DPC-MD. We improve DPC by using a new similarity criterion to deal with the three types of data: numerical, categorical, or mixed data. Compared to other methods for mixed data, DPC absolutely has more advantages to deal with non-spherical distribution data. In addition, the core of the proposed method is based on a new similarity measure for mixed data. This similarity measure is proposed to avoid feature transformation and parameter adjustment. The performance of our method is demonstrated by experiments on some real-world datasets in comparison with that of traditional clustering algorithms, such as K-Modes, K-Prototypes EKP and SBAC. (C) 2017 Elsevier B.V. All rights reserved.
关键词Data clustering Density peaks Entropy Mixed data
DOI10.1016/j.patrec.2017.07.001
收录类别SCI
语种英语
资助项目Fundamental Research Funds for the Central Universities[2017XKZD03]
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000411765800008
出版者ELSEVIER SCIENCE BV
引用统计
被引频次:44[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/6805
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Ding, Shifei
作者单位1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
3.Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China
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Du, Mingjing,Ding, Shifei,Xue, Yu. A novel density peaks clustering algorithm for mixed data[J]. PATTERN RECOGNITION LETTERS,2017,97:46-53.
APA Du, Mingjing,Ding, Shifei,&Xue, Yu.(2017).A novel density peaks clustering algorithm for mixed data.PATTERN RECOGNITION LETTERS,97,46-53.
MLA Du, Mingjing,et al."A novel density peaks clustering algorithm for mixed data".PATTERN RECOGNITION LETTERS 97(2017):46-53.
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